{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llm-stack","slug":"llm-stack","name":"LLM Stack","type":"platform","url":"https://llmstack.ai/","page_url":"https://unfragile.ai/llm-stack","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llm-stack__cap_0","uri":"capability://automation.workflow.visual.agent.workflow.builder.with.drag.and.drop.composition","name":"visual agent workflow builder with drag-and-drop composition","description":"Provides a no-code canvas interface for constructing LLM agent workflows by connecting pre-built blocks (LLM calls, tool integrations, data transformations, branching logic) without writing code. The builder likely uses a directed acyclic graph (DAG) execution model where each block represents a discrete step, with data flowing between blocks via typed connections. Users define agent behavior through visual composition rather than imperative code.","intents":["Build a multi-step LLM agent workflow without writing Python or JavaScript","Prototype an agent that chains LLM calls with API integrations in minutes","Create conditional logic in an agent (if-then branching) using visual blocks instead of code","Reuse pre-built agent patterns across multiple projects"],"best_for":["Non-technical product managers building proof-of-concept agents","Business analysts automating workflows with LLMs","Teams prototyping agent ideas before engineering investment"],"limitations":["Visual composition may become unwieldy for highly complex agents with 20+ steps or deep nesting","Limited ability to express custom logic beyond provided block types without fallback to code","No version control or collaborative editing built into canvas (typical for no-code platforms)"],"requires":["Web browser with modern JavaScript support","Account on LLM Stack platform","API keys for any external LLM providers (OpenAI, Anthropic, etc.) or local LLM access"],"input_types":["text prompts","structured data (JSON)","file uploads","API responses"],"output_types":["text responses","structured JSON","API calls","file exports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_1","uri":"capability://tool.use.integration.pre.built.llm.provider.abstraction.with.multi.model.support","name":"pre-built llm provider abstraction with multi-model support","description":"Abstracts away provider-specific API differences (OpenAI, Anthropic, Cohere, local models) behind a unified interface, allowing users to swap LLM providers or models within an agent without rebuilding the workflow. Likely implements a provider adapter pattern where each LLM provider has a standardized wrapper that normalizes request/response formats, token counting, and error handling.","intents":["Switch from GPT-4 to Claude mid-project without rewriting agent logic","Compare outputs from multiple LLM providers on the same prompt","Use a local open-source model (Llama, Mistral) as a drop-in replacement for cloud APIs","Reduce vendor lock-in by abstracting provider selection"],"best_for":["Teams evaluating multiple LLM providers for cost/quality tradeoffs","Builders wanting to avoid vendor lock-in to a single LLM provider","Organizations with hybrid cloud/on-premise requirements"],"limitations":["Abstraction layer may not expose advanced provider-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking)","Token counting and pricing calculations may differ slightly from native provider SDKs","Latency overhead from adapter layer (~50-100ms per request in typical implementations)"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, Cohere, etc.)","Or local LLM setup (Ollama, vLLM) for on-premise models"],"input_types":["text prompts","system prompts","chat history","structured parameters (temperature, max_tokens)"],"output_types":["text completions","structured JSON (if model supports)","token usage metadata","provider-specific metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_10","uri":"capability://automation.workflow.pre.built.agent.templates.and.examples","name":"pre-built agent templates and examples","description":"Provides a library of pre-built agent templates for common use cases (customer support, data analysis, content generation, etc.), allowing users to clone and customize templates rather than building from scratch. Templates include pre-configured workflows, prompts, tools, and parameters. Likely stored in a template marketplace with metadata (use case, required tools, difficulty level) and versioning.","intents":["Quickly bootstrap a customer support agent by cloning a template","Learn how to build agents by examining example templates","Reuse a template across multiple projects with minor customizations","Share custom agent templates with team members"],"best_for":["Non-technical users wanting to build agents without starting from scratch","Teams building multiple similar agents and wanting to standardize on templates","Builders learning LLM Stack by example"],"limitations":["Templates may not fit all use cases; customization required for domain-specific requirements","Template quality varies; some may be outdated or not reflect best practices","No built-in template versioning or update mechanism; users must manually update cloned templates"],"requires":["LLM Stack account","Understanding of the use case the template addresses"],"input_types":["template metadata (use case, description, tools)","template definitions (workflow, prompts, parameters)"],"output_types":["cloned agent definition","template documentation","example outputs"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_11","uri":"capability://automation.workflow.collaborative.agent.development.with.team.workspaces","name":"collaborative agent development with team workspaces","description":"Supports team collaboration on agent development through shared workspaces, allowing multiple users to view, edit, and deploy agents together. Likely implements role-based access control (RBAC) to manage permissions (viewer, editor, admin) and activity logs to track who made changes. May include commenting or annotation features for feedback on agent definitions.","intents":["Allow multiple team members to collaborate on building an agent","Control who can edit, deploy, or delete agents through role-based permissions","Track changes to agent definitions and who made them","Share agent templates or configurations with team members"],"best_for":["Teams building agents collaboratively","Organizations with governance requirements for agent changes","Builders wanting to share agent knowledge across teams"],"limitations":["No built-in conflict resolution for simultaneous edits; last-write-wins or manual merge required","Activity logs may not capture all changes (e.g., parameter tweaks in LLM calls)","Commenting/annotation features may be limited compared to specialized collaboration tools"],"requires":["LLM Stack account with team/workspace features","User management and authentication system"],"input_types":["agent definitions","user roles and permissions","comments/annotations"],"output_types":["shared agent definitions","activity logs","permission matrices","change history"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_12","uri":"capability://code.generation.editing.custom.code.execution.within.agent.workflows","name":"custom code execution within agent workflows","description":"Allows users to write custom code (Python, JavaScript, etc.) as a step within an agent workflow, bridging the gap between no-code and code-based approaches. Custom code blocks can access workflow context (previous step outputs, agent inputs) and return results that flow to subsequent steps. Likely executes code in a sandboxed environment with timeout and resource limits for safety.","intents":["Implement custom business logic that isn't available as a pre-built block","Transform data between steps using code instead of visual blocks","Call a library or SDK that isn't available as a pre-built integration","Gradually migrate from no-code to code-based agent development"],"best_for":["Teams needing to extend no-code agents with custom logic","Builders with some coding experience wanting more flexibility","Organizations with existing code libraries they want to reuse"],"limitations":["Custom code execution adds latency (~100-500ms per code block depending on complexity)","Sandboxed environment may restrict access to system resources or external libraries","Debugging custom code is harder than debugging visual blocks; limited error messages"],"requires":["Knowledge of Python or JavaScript (depending on platform support)","Understanding of workflow context and data flow"],"input_types":["Python or JavaScript code","workflow context (previous outputs, agent inputs)","external library imports"],"output_types":["code execution results","error messages","logs"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_2","uri":"capability://tool.use.integration.tool.and.api.integration.registry.with.schema.based.binding","name":"tool and api integration registry with schema-based binding","description":"Provides a registry of pre-configured integrations (REST APIs, databases, third-party services) that agents can invoke as tools. Uses a schema-based approach where each tool is defined by its input/output schema, allowing the LLM to understand what parameters it accepts and what it returns. Likely implements automatic schema generation from OpenAPI specs or manual schema definition, with runtime binding to actual API endpoints.","intents":["Let an agent call external APIs (Slack, Stripe, Salesforce) without hardcoding credentials","Define a custom tool (e.g., database query) that an agent can invoke as part of its workflow","Automatically generate tool schemas from OpenAPI specifications","Manage API credentials and authentication centrally without exposing them in agent definitions"],"best_for":["Teams building agents that need to interact with existing SaaS platforms","Builders creating agents that require database access or custom backend logic","Organizations with complex API ecosystems wanting centralized tool management"],"limitations":["Pre-built integrations limited to popular platforms; custom APIs require manual schema definition","No built-in retry logic or circuit breaker for flaky external APIs (must be handled in agent logic)","Schema generation from OpenAPI may require manual refinement for complex specs with edge cases"],"requires":["API credentials for external services (stored securely in LLM Stack)","OpenAPI spec or manual schema definition for custom tools","Network access from LLM Stack infrastructure to external API endpoints"],"input_types":["API endpoint URLs","OpenAPI specifications","manual JSON schemas","authentication credentials (API keys, OAuth tokens)"],"output_types":["API responses (JSON, XML, text)","structured tool results","error messages with retry metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_3","uri":"capability://text.generation.language.prompt.template.management.with.variable.substitution.and.versioning","name":"prompt template management with variable substitution and versioning","description":"Provides a prompt template system where users define reusable prompt structures with placeholders for dynamic variables (user input, context, data from previous steps). Supports versioning of prompts, allowing teams to iterate on prompt wording and compare performance across versions. Likely stores templates in a database with metadata (version history, performance metrics, tags) and substitutes variables at runtime using a simple templating engine.","intents":["Create a reusable prompt template that multiple agents can reference","A/B test different prompt wordings to optimize agent output quality","Version control prompts and rollback to a previous version if quality degrades","Inject dynamic data (user context, previous conversation history) into prompts at runtime"],"best_for":["Teams iterating on prompt engineering to improve agent quality","Organizations managing multiple agents with shared prompt patterns","Builders wanting to decouple prompt logic from workflow definitions"],"limitations":["Template syntax limited to simple variable substitution; no conditional logic within templates","No built-in A/B testing framework; requires external analytics to compare prompt versions","Version history may grow large for heavily-iterated prompts, requiring cleanup policies"],"requires":["LLM Stack account with template storage","Understanding of prompt engineering best practices"],"input_types":["text prompt templates","variable definitions (JSON schema)","metadata tags"],"output_types":["rendered prompts (text)","version history metadata","performance metrics (if integrated with logging)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_4","uri":"capability://automation.workflow.agent.execution.and.monitoring.with.step.level.logging","name":"agent execution and monitoring with step-level logging","description":"Executes agent workflows step-by-step, capturing detailed logs at each step (LLM input/output, tool calls, latency, errors). Provides a dashboard or UI to monitor running agents, view execution history, and debug failures. Likely implements a state machine for agent execution where each step is tracked with timestamps, inputs, outputs, and error information, stored in a database for later analysis.","intents":["Debug why an agent produced an unexpected output by reviewing step-by-step execution logs","Monitor agent performance in production (latency, error rates, token usage)","Replay a failed agent execution to understand what went wrong","Export execution logs for compliance or audit purposes"],"best_for":["Teams running agents in production and needing observability","Builders debugging complex multi-step agent workflows","Organizations with compliance requirements for audit trails"],"limitations":["Logging all step details can generate large volumes of data; retention policies may limit historical access","Real-time monitoring dashboard may have latency (typically 5-30 seconds behind actual execution)","Sensitive data in logs (API responses, user inputs) requires careful handling and redaction policies"],"requires":["LLM Stack account with logging/monitoring enabled","Network connectivity to LLM Stack backend for log transmission"],"input_types":["agent execution events","step metadata","error traces"],"output_types":["execution logs (JSON or structured format)","performance metrics (latency, token usage)","error reports","audit trails"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_5","uri":"capability://automation.workflow.scheduled.and.event.triggered.agent.execution","name":"scheduled and event-triggered agent execution","description":"Allows agents to be triggered on a schedule (cron-like) or in response to external events (webhooks, message queue events, API calls). Implements a scheduler that manages periodic execution and an event listener that captures external triggers and queues agent runs. Likely uses a job queue (Redis, RabbitMQ) to manage execution and ensure reliability with retry logic.","intents":["Run an agent every hour to process new data and send notifications","Trigger an agent when a webhook is received from an external service (e.g., Stripe payment event)","Execute an agent in response to a message in a queue (e.g., Kafka topic)","Set up a daily report generation agent that runs at a specific time"],"best_for":["Teams building autonomous agents that run without user interaction","Builders integrating agents into event-driven architectures","Organizations automating recurring business processes"],"limitations":["Scheduled execution timing may have drift (typically ±5 minutes) depending on platform load","No built-in distributed tracing across multiple triggered executions; requires external APM","Webhook delivery is not guaranteed at-least-once without explicit retry configuration"],"requires":["LLM Stack account with scheduling/event features enabled","Cron expression syntax for scheduled triggers, or webhook URL for event triggers","Network access from LLM Stack to external event sources (webhooks, queues)"],"input_types":["cron expressions","webhook payloads (JSON)","queue messages","event metadata"],"output_types":["agent execution results","execution status (success/failure)","webhook response (if applicable)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_6","uri":"capability://data.processing.analysis.agent.input.output.validation.with.schema.enforcement","name":"agent input/output validation with schema enforcement","description":"Enforces structured input and output schemas for agents, validating that inputs match expected types and formats before execution, and validating agent outputs against a defined schema. Uses JSON Schema or similar schema definition language to specify constraints (required fields, data types, value ranges). Likely validates inputs at agent invocation time and outputs after LLM generation, with clear error messages for validation failures.","intents":["Ensure an agent only accepts properly formatted input (e.g., valid email addresses, phone numbers)","Validate that an agent's output conforms to a required structure (e.g., JSON with specific fields)","Reject invalid inputs early to avoid wasting LLM tokens on malformed requests","Generate API documentation automatically from agent input/output schemas"],"best_for":["Teams deploying agents as APIs with strict contract requirements","Builders integrating agents into systems that require structured data","Organizations with compliance requirements for data validation"],"limitations":["Schema validation adds latency (~10-50ms per request) for complex schemas","LLM outputs may not always conform to schema; requires fallback logic or re-prompting","Schema definition requires upfront design; changes to schemas may break existing integrations"],"requires":["JSON Schema or similar schema definition language","Clear understanding of expected input/output formats"],"input_types":["JSON schemas","data type definitions","validation rules"],"output_types":["validation results (pass/fail)","error messages with details","validated/coerced data"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_7","uri":"capability://memory.knowledge.multi.step.conversation.management.with.context.persistence","name":"multi-step conversation management with context persistence","description":"Manages multi-turn conversations where agents maintain context across multiple user interactions. Stores conversation history (user messages, agent responses, tool calls) in a database and retrieves relevant context for each new user message. Likely implements a conversation state machine where each turn updates the conversation state and context window is managed to fit within LLM token limits.","intents":["Build a chatbot that remembers previous conversation context and refers back to it","Maintain conversation state across multiple user interactions without losing context","Implement conversation branching (user can explore different paths in a conversation)","Export full conversation history for analysis or compliance"],"best_for":["Teams building conversational agents (chatbots, customer service bots)","Builders creating agents that need to maintain state across sessions","Organizations with compliance requirements for conversation logging"],"limitations":["Context window management becomes complex with long conversations; may require summarization or truncation","Storing full conversation history can be expensive for high-volume deployments","No built-in conversation branching UI; requires custom implementation for multi-path conversations"],"requires":["Database for storing conversation history","Session management or user authentication to associate conversations with users"],"input_types":["user messages (text)","conversation metadata (user ID, session ID)","context hints (previous topics, user preferences)"],"output_types":["agent responses (text)","conversation history (JSON)","context summaries"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_8","uri":"capability://automation.workflow.agent.deployment.and.versioning.with.rollback.capability","name":"agent deployment and versioning with rollback capability","description":"Provides version control for agent definitions, allowing users to deploy specific versions and rollback to previous versions if needed. Likely stores agent definitions (workflow, prompts, tools, parameters) as versioned artifacts in a database, with metadata tracking deployment history, performance metrics per version, and rollback mechanisms. Implements a deployment pipeline where new versions can be tested before production rollout.","intents":["Deploy a new version of an agent to production and monitor its performance","Quickly rollback to a previous agent version if the new version has quality issues","A/B test two agent versions simultaneously to compare performance","Track which agent version is currently in production and when it was deployed"],"best_for":["Teams running agents in production with high availability requirements","Builders iterating on agent logic and needing safe deployment practices","Organizations with change management and audit requirements"],"limitations":["Rollback is instantaneous for agent logic but may not rollback side effects (e.g., API calls already made)","A/B testing requires traffic splitting logic; not built-in to all deployments","Version history can grow large; requires cleanup policies for old versions"],"requires":["LLM Stack account with versioning/deployment features","Understanding of deployment best practices"],"input_types":["agent definitions (workflow, prompts, tools)","deployment metadata (target environment, traffic percentage)"],"output_types":["deployment status (success/failure)","version history","performance metrics per version","rollback confirmation"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llm-stack__cap_9","uri":"capability://data.processing.analysis.cost.tracking.and.token.usage.analytics","name":"cost tracking and token usage analytics","description":"Tracks LLM API costs and token usage across agent executions, providing analytics dashboards and cost breakdowns by model, agent, or time period. Likely integrates with LLM provider billing APIs to fetch actual costs and combines with local token counting to provide accurate cost estimates. Stores usage data in a database and provides aggregation/filtering for analysis.","intents":["Monitor total LLM costs across all agents to manage budget","Identify which agents or models are most expensive","Estimate costs before deploying a new agent version","Set up cost alerts when spending exceeds a threshold"],"best_for":["Teams managing multiple agents and needing cost visibility","Builders optimizing agents for cost efficiency","Organizations with strict budget controls and cost allocation requirements"],"limitations":["Cost estimates may differ from actual provider billing due to rounding or pricing changes","Token counting varies by model; estimates may be inaccurate for non-standard models","Real-time cost tracking has latency (typically 1-24 hours behind actual provider billing)"],"requires":["API keys for LLM providers (to fetch billing data)","LLM Stack account with cost tracking enabled"],"input_types":["agent execution events","token usage data","model pricing information"],"output_types":["cost reports (by agent, model, time period)","token usage analytics","cost forecasts","billing alerts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support","Account on LLM Stack platform","API keys for any external LLM providers (OpenAI, Anthropic, etc.) or local LLM access","API keys for at least one supported LLM provider (OpenAI, Anthropic, Cohere, etc.)","Or local LLM setup (Ollama, vLLM) for on-premise models","LLM Stack account","Understanding of the use case the template addresses","LLM Stack account with team/workspace features","User management and authentication system","Knowledge of Python or JavaScript (depending on platform support)"],"failure_modes":["Visual composition may become unwieldy for highly complex agents with 20+ steps or deep nesting","Limited ability to express custom logic beyond provided block types without fallback to code","No version control or collaborative editing built into canvas (typical for no-code platforms)","Abstraction layer may not expose advanced provider-specific features (e.g., OpenAI's vision capabilities, Anthropic's extended thinking)","Token counting and pricing calculations may differ slightly from native provider SDKs","Latency overhead from adapter layer (~50-100ms per request in typical implementations)","Templates may not fit all use cases; customization required for domain-specific requirements","Template quality varies; some may be outdated or not reflect best practices","No built-in template versioning or update mechanism; users must manually update cloned templates","No built-in conflict resolution for simultaneous edits; last-write-wins or manual merge required","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=llm-stack","compare_url":"https://unfragile.ai/compare?artifact=llm-stack"}},"signature":"Oi3+kccDffEmr9QfdCC4MEhZVJc7P25r9tGpzPTv42DwzTf/HkebVWnzlHm3QNx3CFco4r36ALjQs89GZNwUCQ==","signedAt":"2026-06-20T12:00:04.323Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llm-stack","artifact":"https://unfragile.ai/llm-stack","verify":"https://unfragile.ai/api/v1/verify?slug=llm-stack","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}